Fluid Dynamics
Realizability-Constrained Machine Learning for Turbulence Closures in Wake Flows
Talib Ansari, Priyank H. Mehta and Harshal D. Akolekar
Computational fluid dynamics (CFD)-driven machine learning frameworks based on symbolic regression offer a promising pathway for
turbulence model discovery, but are often hindered by numerical instability, residual stagnation, and non-physical model behavior during training. In particular, realizability, which is rarely enforced explicitly during model development, remains a critical yet overlooked requirement, especially for accurate wake prediction. In this work, a residual- and realizability-filtered CFD-driven framework is proposed to enhance both efficiency and robustness within a gene expression programming (GEP) paradigm. The method integrates two residual-based filtering criteria along with a barycentric-map-based realizability constraint directly into the CFD solution loop, enabling early identification and rejection of unstable and non-realizable candidate models. This reduces unnecessary computational effort while guiding the search toward physically admissible solutions. The proposed approach achieves a 42.3% reduction in computational cost relative to the baseline CFD-driven GEP framework and reduces non-realizable models at convergence from 58.4% to 1.7%. The framework is trained on a canonical cylinder wake. The resulting models enhance mean wake prediction and remain realizable across training and test cases, with robust generalization to diverse geometries and operating conditions, including a rectangular cylinder, an airfoil, and an axisymmetric body. The study further provides insights into realizable model statistics, coefficient trends, and conditions governing physically consistent wake behavior. These results demonstrate that incorporating realizability and stability constraints within CFD-driven learning enables efficient and physically consistent
turbulence model discovery, offering a scalable pathway toward reliable data-driven closure development.
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Instrumentation and Detectors · Cross-listed from quant-ph
Cryogenic Systems for Quantum Photonic Technologies: A Practical Review
Alex H. Rubin, Victoria A. Norman and Marina Radulaski
While nonclassical light sources are fundamental to
quantum communication and computing, solid-state platforms like color centers and
quantum dots require cryogenic temperatures to reach the performance levels necessary for practical applications. Over the past decade, low-temperature engineering has transitioned from manual handling of
liquid cryogens to automated closed-cycle cryostats. This review details the principles behind modern cooling hardware ranging from flow cryostats to mechanical cryocoolers and dilution refrigerators, with a specific focus on the requirements of optical
quantum devices. Aimed at the practicing scientist, this overview provides the technical insights and historical context needed to navigate the current cryogenic landscape and evaluate its role in the future of
quantum technology deployment.
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Accelerator Physics
Approximate Invariant Analysis: An Efficient Framework for Nonlinear Beam Dynamics, Part I: Geometric Approaches of the Poincaré Rotation Number
Yongjun Li, Sergei Nagaitsev, Derong Xu et al. (+2 more)
We present the first part of an efficient framework for nonlinear beam dynamics, termed Approximate Invariant Analysis (AIA). The framework is based on the construction of approximate invariants~[Y.~Li, D.~Xu, and Y.~Hao, Phys.\ Rev.\ Accel.\ Beams 28, 074001 (2025)] and on the extraction of the betatron frequency with the
geometric foundations of Poincaré rotation number~[S.~Nagaitsev and T.~Zolkin, Phys.\ Rev.\ Accel.\ Beams 23, 054001 (2020)]. The method is demonstrated using the National Synchrotron Light Source~II (NSLS-II) storage ring as an illustrative example.
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All authors Yongjun Li, Sergei Nagaitsev, Derong Xu, Yue Hao, Chad Mitchell
Fluid Dynamics
Interfacial waves from pressure forcing: role of the transients
Vinod Kumar Kadari, Nikhil Yewale, Palas Kumar Farsoiya et al. (+2 more)
A localised overpressure translating at a uniform speed greater than a critical value acts at the interface between two deep fluid layers with different densities. We analyse the resulting wave patterns using an initial-value problem formulation within the linearised, inviscid, potential flow framework. The steady-state interface exhibits short capillary waves ahead of the forcing and long
gravity waves behind it, arising from an asymmetric cancellation of
Fourier components in the far field. The time-dependent part of the solution, decaying algebraically with time, plays a crucial role in this mechanism. This contrasts with classical steady approaches, which require additional conditions to select a unique solution. We extend this approach to a two-fluid interface and validate the predictions against nonlinear simulations.
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All authors Vinod Kumar Kadari, Nikhil Yewale, Palas Kumar Farsoiya, Y. S. Mayya, Ratul Dasgupta
Instrumentation and Detectors · Cross-listed from hep-ex
Characterization of large diameter ultra-thin vacuum windows for soft X-ray applications
K. Desch, E. Ferrer-Ribas, F.J. Iguaz et al. (+3 more)
We present novel, ultra-thin, large-diameter silicon nitride windows for various soft X-ray applications. Together with the company NORCADA, we developed windows with 200 nm and 300 nm thickness withstanding pressure differences above 1 bar. The windows have an open diameter of 14 mm. They were intensively
vacuum- and overpressure-tested, showing very good results. At a measurement campaign at the synchrotron radiation source SOLEIL in France, the transparency of the windows was measured over a range from 50 eV to 15 keV, giving results comparable with the expected transparencies.
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All authors K. Desch, E. Ferrer-Ribas, F.J. Iguaz, J. von Oy, A. Quintana, T. Schiffer
Atomic Physics
Analytical emission model for the design of primary effusive sources
I. N. Ashkarin, J. Cheayto, P. Cheinet et al. (+2 more)
We present an analytical emission model that accurately predicts the properties of effusive sources formed by long collimation tubes. By construction, it captures the full range of molecular flow, from the transparent flux regime, which occurs in highly rarefied gases, to the opaque regime, which arises as the flux increases and interparticle collisions become non-negligible. The model is based on a previously developed secondary-emission-surface approach, improved here to overcome its internal limitations and recover the well-established axial flux intensity. It provides accurate analytical predictions of the angular intensity distribution in the molecular flow regime, offering valuable guidance for the design of efficient primary sources across a broad range of experiments in atomic and molecular physics
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All authors I. N. Ashkarin, J. Cheayto, P. Cheinet, D. Comparat, S. Lepoutre
Chemical Physics
Asymmetric Planar-to-Dewar Isomerisation in BN-Doped Naphthalene: Mechanistic Implications for Molecular Solar Thermal Storage
Michael Bühler and Merle I. S. Röhr
The planar to Dewar valence isomerisation of 4a,8a-azaboranaphthalene (BN$_\text{Naph}$), a $\pi$ extended BN-doped analogue of azaborine, is investigated to evaluate how BN incorporation reshapes the minimum energy pathway on the ground state. This process is, for example, relevant in the context of molecular
solar thermal (MOST) energy storage, where absorbed sunlight is converted into chemical energy through reversible photoisomerisation. Structures and vertical excitations were computed using DFT and TD-DFT, minimum energy pathways were mapped with nudged elastic band (NEB) calculations, and pathway energetics were refined with state averaged XMS-CASPT2. In addition, azaborine was examined as a comparison system, with particular emphasis on whether substituents at nitrogen and boron promote Dewar formation. The effect of BN doping on the system was analysed in detail. Compared with the carbon analogue, the conversion pathway becomes asymmetric with a metastable intermediate stabilized by a transient boron to carbon contact. The transition structure closely resembles an S$_0$/S$_1$ conical intersection, which is consistent with a vibrationally activated nonradiative funnel. For tuning MOST properties, screening of single substituents across the whole molecule reveals predominantly red shifted S$_1$ energies together with increased oscillator strengths and indicates that appropriate substitution can improve Dewar formation in azaborine derivatives.
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Fluid Dynamics
Structured input-output analysis of oblique turbulent bands in Waleffe flow
Jino George and Chang Liu
This work employs structured input-output analysis (SIOA) to study Waleffe flow. The SIOA framework employs structured uncertainty to include the componentwise structure of nonlinearity in Navier-Stokes equations, and SIOA quantifies the flow response using structured singular values. The structured input-output analysis identifies the wavelength and inclination angle of oblique turbulent bands observed in large-domain direct numerical simulations. The structured input-output response scales over Reynolds number as $\sim Re^{1.7}$.
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Instrumentation and Detectors
Machine Learning for neutron source distributions
Jose Ignacio Robledo, Norberto Schmidt, Klaus Lieutenant et al. (+3 more)
In light of the recent advancements in machine learning, we propose a novel approach to
neutron source distribution estimation through the utilisation of probabilistic generative models. The estimation is based on a Monte Carlo particle list, which is only required during the training stage of the machine learning model. Once the source distribution has been learned, the model is independent of the original particle list, allowing for further sampling in an efficient, rapid, and
memory-costless manner. The performance of various generative models is evaluated, including a variational autoencoder, a normalizing flow, a generative
adversarial network, and a denoising
diffusion model. These approaches are then compared to existing source distribution estimations, and the advantages and disadvantages of each approach are discussed. The results demonstrate that source distributions can be modeled through the use of probabilistic generative models, which paves the way for further advancements in this field.
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All authors Jose Ignacio Robledo, Norberto Schmidt, Klaus Lieutenant, Jingjing Li, Stefan Kesselheim, Paul Zakalek
Medical Physics · Cross-listed from eess.IV
A Comparative Analysis of CT Degradation for LDCT Nodule Classification using Radiomics
Jiaying Liu, Anna Corti, Valentina D.A. Corino et al. (+1 more)
Low-dose computed
tomography (LDCT) is the standard modality for lung cancer screening, known for its low radiation dose but high noise levels. While existing literature focuses on denoising LDCT images, comparative research on simulating LDCT characteristics to directly use these images for model development is lacking. This study shifts the focus from denoising images to degrading available standard-dose CT (SDCT) data, generating synthetic images for data augmentation to train classifiers for screening-detected nodules. We compare three degradation methods: (1) a sinogram domain statistical noise insertion; (2) replicate a validated physics-based simulation using Pix2Pix; and (3) unpaired CycleGAN. The generated images were utilized to simulate LDCT screening scenario replacing 695 SDCT cases from the LIDC-IDRI
dataset, from which radiomic features were extracted to train machine learning models for lung nodule classification. Regarding image quality, CycleGAN achieved the best Fréchet inception distance (0.1734) and kernel inception distance (0.0813; 0.1002) scores, indicating distributional
alignment with the target low-dose domain. In the nodule classification task, results confirmed the necessity of domain adaptation since a baseline model trained on non-degraded SDCT data failed to generalize to the real LDCT set (AUC 0.789) with a low sensitivity (0.571). Degraded images generated using CycleGAN approach led to the most balanced performance on the classification task using Adam Booster classifier, achieving an AUC of 0.861, sensitivity of 0.743 and specificity of 0.858 in the independent test. Our findings confirm that generating synthetic LDCT data from standard-dose scans is a viable strategy for training robust nodule classifiers for screening detected nodules.
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All authors Jiaying Liu, Anna Corti, Valentina D.A. Corino, Luca Mainardi
Plasma Physics
High-order exponential solver method for particle-in-cell simulations in cylindrical geometry
Szilárd Majorosi, Nasr A.M. Hafz and Zsolt Lécz
Recent developments in high peak-power table-top
laser systems reaching highly relativistic light intensities have led to significant advances in
laser-driven particle acceleration schemes (mainly the
laser wakefield acceleration, LWFA) that heavily rely on particle-in-
cell (PIC) simulations for the microscopic understanding of the acceleration process. Efficient
algorithms have been developed by taking advantage of the cylindrical geometry of the
laser-
plasma acceleration interaction, which reduces the computational and
memory costs of these simulations, but with the trade-off of reduced accuracy compared to the 3D simulations. The most successful solution solves the Maxwell equations on a
Fourier-Bessel spectral basis in this geometry, as used by the well-known FBPIC code. In this work, we present a solution that is a real-space equivalent of the latter using the finite difference exponential time-domain method. Spatially, we represent the derivatives with high-order staggered finite differences locally and address issues of the near-axis particle representation. Additionally, we also develop an exponential solution to propagate the
laser envelope potential with high accuracy in the cylindrically symmetric PIC model. We show that this method provides a very high accuracy without relying on a transformation to special basis functions. We verified the accuracy and the convergence of these methods in various
benchmarks involving
laser propagation in
vacuum and in underdense
plasma. Electron injection in the non-linear
laser wakefield regime has also been simulated and the results are compared with 3D simulations, and to the cylindrical spectral solution of FBPIC. We found good agreement between these methods; however, the spectral solution resulted in less energetic electrons and a smoother spatial distribution near the cylindrical axis.
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Computational Physics · Cross-listed from cond-mat.stat-mech
Identifying the relevant parameters in design strategies for stable glasses
Leonardo Galliano and Ludovic Berthier
A glass is conventionally obtained by cooling a bulk supercooled
liquid through its glass transition temperature. The discovery of ultrastable glasses prepared using physical vapor deposition, together with the recent multiplication of numerical
algorithms created to increase the stability of glasses, demonstrates the existence of a variety of strategies for designing glasses with different physical properties. This raises a broader question: which parameters most strongly govern the enhancement of glass stability? Existing computational strategies often produce highly stable glasses by optimizing certain physical properties through dynamical changes in particle diameters. We challenge the idea that these physical quantities are causally responsible for glass stability and suggest instead that diameter dynamics is the principal source of enhanced stability. To support our view, we introduce computational methods to optimize physical quantities without changing the particle diameters. Using the examples of enhanced hyperuniformity at large scale and local ordering at small scale, we design glass configurations with highly optimized values compared to bulk
equilibrium states. However, these glasses do not show enhanced stability. The proposed physical quantities are correlated with glass stability, but are not causally responsible for ultrastability. These findings indicate that design rules for stable glasses should be reinterpreted in terms of the dynamical processes that generate stability, rather than the optimized physical quantities they target.
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Plasma Physics
MPEX AI Digital Twins Milestone Report
Gary Staebler, Rhea Barnett, Mark Cianciosa et al. (+13 more)
This is the six month progress report to Fusion Energy Science (FES) and the American Science Cloud (AmSC) on the MPEX AI Digtial Twins project that was started in October 2025. There are two milestones to demonstrate the Artificial Intelligence (AI) advantage for MPEX operations and scientific discovery, that will be completed by June 2026. The first is a Helicon AI Hot-Spot Controller (Sec. 3.1), which is the helicon heating component of the more comprehensive planned MPEX AI Hot Spot Digital Twin (Sec. 3). The second is an E-beam Damage Assessment Digital Twin (Sec. 4.1), which is a reduced electron beam damage modality prototype for the MPEX AI Damage Assessment Digital Twin (Sec. 4). These two phase I milestones are on track for the June demonstration. In addition to these two milestones, progress on configuring the
Galaxy software interface for automation, validation and data analysis is reported (Sec. 5). This interface now connects a subset of the main physics simulation codes to DOE HPC resources and will connect to the MPEX data acquisition system so that analysis of data, validation and execution of simulations can be performed by the scientist or by AI-
Agents. When AmSC is ready to accept connections and data,
Galaxy will be the MPEX interface to AmSC
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All authors Gary Staebler, Rhea Barnett, Mark Cianciosa, Rinkle Juneja, Atul Kumar, Wouter Tierens, Minglei Yang, Cory Hauck, Richard Archibald, Viktor Reshniak, Pablo Seleson, Sam Reeve, Gregory Watson, John Duggan, Ben Dudson, Vasily Geyko
Biological Physics · Cross-listed from cond-mat.soft
Cell divisions suppress dynamical correlations in solid tissues
Ali Tahaei, Ahandeep Manna and Marko Popović
Developing tissues often maintain mechanical coherence while continuously remodeling through cellular processes such as
cell divisions and rearrangements. In this way, they are an example of amorphous solids. In passive amorphous solids, local rearrangements can trigger one another through long-ranged elastic interactions, leading to system-spanning avalanches near yielding. Whether similar collective dynamics should be expected in living tissues is unclear, because
cell divisions generate stress and remodeling events independently of local mechanical stability. Here, we address this question using a two-dimensional elastoplastic model in which
cell divisions are treated as active plastic events. We find that while
cell divisions fluidize the tissue below the passive yield stress, but preserve the marginal stability in the quasistatic limit. However, they also strongly suppress the system-spanning avalanches of
cell rearrangements, in constrast with the expected behavior in passive amorphous solids. Finally, we show that the avalanche supression originates from the energy balance in the system. Namely, the energy injected by
cell divisions allows for shear flow below the yield stress, but also provides a finite budget for rearrangements. These results suggest that proliferating tissues display the structural hallmarks of marginal amorphous solids while exhibiting much shorter-ranged correlations in dynamics, compared to passive amorphous solids.
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Atomic and Molecular Clusters
Hyperfine-Resolved Rovibrational and Rotational Spectroscopy of OH$^+$ ($X ^3\Sigma^-$)
Weslley G. D. P. Silva, Lea Schneider, Urs U. Graf et al. (+6 more)
The OH$^+$ ($X ^3\Sigma^-$) radical cation has been investigated by combining a 4 K 22-pole ion trap apparatus with high-resolution IR and THz radiation sources. Applying different types of action spectroscopic methods, the fundamental vibrational band in the 3 $\mu$m range and the
spin manifold of the $N=1 \leftarrow 0$ rotational transition around 1 THz have been extended and refined. Additionally, the
spin manifold of the $N=2 \leftarrow 1$ rotational transition, scattered around 2 THz, has been measured for the first time with microwave accuracy. Although all hyperfine components of the pure rotational transitions are affected by considerable Zeeman splittings, a simulation of their contours allowed us to extract the field-free center frequencies with high accuracy. A global fit combining rovibrational and pure rotational transitions from the literature with those newly obtained in this work was performed, leading to improvements in the spectroscopic constants of OH$^+$, particularly those in the ground vibrational state.
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All authors Weslley G. D. P. Silva, Lea Schneider, Urs U. Graf, Holger S. P. Müller, Pavol Jusko, Arshia M. Jacob, Dominik Riechers, Stephan Schlemmer, Oskar Asvany
Physics and Society
Empirical Confirmation of the Environmental-Dominance Inequality A direct decomposition of Var(ln h̊o}eff ) across four levels of aggregation
Kristian Sestak
Empirical confirmation of the environmental-dominance inequality Var(ln rho_eff) >> Var(ln k) from arXiv:2605.02985, computed directly from three public
datasets (Opportunity Atlas, World Bank GDP per capita PPP, World Inequality Database) at four levels of aggregation: U.S. census tracts, between countries, within-country deciles, and the global pooled-individual distribution. The headline global value Var(ln rho_eff) = 4.33 yields a dominance ratio R in [27, 134] across plausible sigma_ln k in [0.18, 0.40]. The inequality holds with one-to-two orders of magnitude margin at the global and within-country-decile levels, with a single-digit but still dominant margin between countries, and collapses to R in [0.33, 1.61] within already-homogenized U.S. census tracts for income. A 1990-2022 time series shows the global aggregate stable while composition shifts from between-country dispersion (-34%) to within-country dispersion (+26%), consistent with international convergence plus Piketty r > g. Multi-outcome validation shows the inequality is robust for income, infant mortality and incarceration but shrinks toward parity for outcomes targeted by sustained global convergence (life expectancy). Partial-identification and selection-bias bounds (Chetty-style 40-50% selection share) leave R in [14, 80]. All inputs and outputs are SHA-256 hashed in an append-only manifest and fully reproducible from the accompanying notebooks.
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Atomic Physics
Observation of Magnetically-Induced atomic transitions of the Cs 6S$_{1/2} \rightarrow 7$P$_{3/2}$ line at 456 nm
Armen Sargsyan, Arevik Amiryan, Emmanuel Klinger et al. (+1 more)
It has recently been demonstrated that magnetically induced (MI) transitions, a class of transitions forbidden at zero
magnetic field, of the Cs 6$^2$S$_{1/2} \rightarrow 6^2$P$_{3/2}$ (D$_2$) line, exhibit promising features for high-resolution physics applications in the near-infrared range. In this work, we study a group of seven MI transitions ($F_g = 3 \rightarrow F_e = 5$) of the Cs $6^2$S$_{1/2} \rightarrow 7^2$P$_{3/2}$ line at $\lambda = 456$ nm. The experimental measurements are in very good agreement with theoretical predictions based on the diagonalization of the Zeeman
Hamiltonian. In
magnetic fields ranging from $0.2-3$ kG, these transitions reach a maximum intensity above that of conventional transitions. Another noteworthy property is their large frequency shift, reaching approximately $17~\mathrm{GHz}$ with respect to the unperturbed hyperfine transitions in
magnetic fields of about $3~\mathrm{kG}$. These interesting properties may prove useful for the realization of optical frequency references or magnetometers with sub-micron spatial resolution in the blue region of the spectrum.
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All authors Armen Sargsyan, Arevik Amiryan, Emmanuel Klinger, David Sarkisyan
Fluid Dynamics
Formulations for scalar boundedness in simulations of turbulent compressible multi-component flows using high-order finite-difference methods
Ye Wang, Armin Wehrfritz and Evatt R. Hawkes
Preserving scalar boundedness is important for numerical schemes used in turbulent compressible multi-component flow simulations to prevent unphysical results and unstable simulations. However, ensuring scalar boundedness for high-order, low-dissipation numerical schemes poses challenges in highly under-resolved conditions due to inherent dispersion errors that generate spurious oscillations. Numerical dissipation is needed to mitigate these oscillations, but excessive dissipation negatively affects resolution. In this work, we propose formulations for high-order finite-difference schemes to preserve scalar boundedness without predefined bounds, while maintaining high accuracy and low numerical dissipation. The proposed formulations augment a non-dissipative numerical flux of a high-order central-difference scheme with an explicit dissipative numerical flux that adaptively switches between high-order and low-order formulations. Building on a deliberate choice of the non-dissipative flux, we construct two schemes using Jameson's artificial viscosity method and a monotonicity-preserving limiter as the dissipative flux. We examine the schemes in one-dimensional scalar advection problems and a three-dimensional temporal turbulent mixing-layer case involving sharp scalar gradients and under-resolved conditions, evaluating their accuracy, boundedness of species mass fractions, and numerical diffusivity. The scheme with the monotonicity-preserving limiter demonstrates superior performance.
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Instrumentation and Detectors
CaloArt: Large-Patch x-Prediction Diffusion Transformers for High-Granularity Calorimeter Shower Generation
Zhengkun Huang and Gongxing Sun
High-granularity calorimeters make ML-based fast shower simulation a high-dimensional generative modeling problem, where voxel-space generators must balance physics fidelity with training and inference cost. This work studies large-patch tokenization with x-prediction, enabling efficient raw voxel generation. We propose CaloArt, a modernized DiT-style backbone with 3D positional encoding and architectural refinements, trained via conditional flow matching with decoupled prediction and loss spaces. On CaloChallenge
Dataset 2, where small patch size remains affordable, v-prediction performs well, and CaloArt achieves the best FPD, strongest high-level metrics, and strongest ResNet classifier metrics. On CaloChallenge
Dataset 3, the 40500-voxel grid makes large patches necessary; x-prediction improves all reported metrics over v-prediction and places CaloArt on the quality-generation-time Pareto frontier. The final CCD2 and CCD3 models both retain O(10) ms single-GPU generation time, with 9.71 and 11.14 ms per shower. These results support large-patch voxel-space
diffusion transformers with x-prediction as a compute-efficient route to high-granularity calorimeter shower synthesis, reducing training and inference cost without a pretrained latent tokenizer.
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Computational Physics · Cross-listed from cs.CE
A geometry-aligned multi-fidelity framework for uncertainty quantification of wildfire spread
Konstantinos Vogiatzoglou, Costas Papadimitriou, Vasilis Bontozoglou et al. (+2 more)
Forward propagation of input uncertainties in physics-based wildfire models is computationally prohibitive, limiting the use of high-fidelity simulators in risk assessment workflows. This work introduces a geometry-aligned bi-fidelity surrogate framework that addresses the convection-dominated nature of wildfire spread by mapping low- and high-fidelity solution snapshots onto a common reference domain prior to basis selection and reconstruction. Unlike conventional bi-fidelity schemes, which combine spatially shifted snapshots and thus suffer from oscillations and excess basis requirements near sharp fronts, the proposed mapping aligns the dominant front geometry through per-variable shift/stretch transforms in 1D and an activity indicator-based affine
alignment in 2D, so that reduced bases compare physically corresponding structures rather than displaced ones. Building on the ADfiRe physics-based simulator, we demonstrate the method on 1D and 2D test cases in which low- and high-fidelity models differ in mesh resolution and physical completeness. Across both settings, the geometry-aligned surrogate reproduces full-field temperature and fuel composition with substantially lower error than its unmapped counterpart, eliminates Gibbs-type oscillations near steep gradients, and recovers high-fidelity probability density functions for key quantities of interest (e.g., maximum temperature, evaporated moisture, and burned area). After offline training, online predictions are roughly three orders of magnitude cheaper than direct high-fidelity evaluation, making the framework a practical building block for many-query uncertainty quantification once the offline cost is amortized over enough queries. We discuss the conditions under which the
geometric alignment is most effective, its limitations for non-convex or topologically complex fronts, and the path toward validation against real data.
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All authors Konstantinos Vogiatzoglou, Costas Papadimitriou, Vasilis Bontozoglou, Petros Koumoutsakos, Han Gao
Instrumentation and Detectors
Development and validation of a forward 0.7–4 MeV quasi-monoenergetic neutron capability at the CN Van de Graaff of LNL
Jeffery Wyss, Pierfrancesco Mastinu, Elizabeth Musacchio González et al. (+8 more)
The CN Van de Graaff accelerator of INFN–LNL provides forward-angle quasi-monoenergetic
neutrons in the 0.7–4 MeV range via the 7Li(p,n)7Be reaction on thin metallic lithium targets. This work describes the development and experimental validation of this forward
neutron capability, combining comparisons of commonly used transport tools with time-of-flight (ToF) measurements.
Neutron yields calculated with EPEN, FLUKA, MCNPX, and PINO are compared over the CN energy range in order to assess model-dependent variations relevant for fluence estimates. For zero incident-energy spread, a mutually consistent set of transport calculations agrees within 5% and is used as a practical reference for normalisation. The effect of incident-energy convolution on the predicted yields is examined. Time-of-flight measurements performed using a sub-nanosecond secondary pulsing system verify the timing structure and forward-angle kinematics of the quasi-monoenergetic
neutron component at the detector position, with
neutron arrival times consistent with the expected forward kinematics within the experimental resolution. Using measured
proton currents and transport calculations based on this reference set, forward
neutron fluences at the device position are estimated with an overall uncertainty of approximately 9%, including contributions from current integration, target thickness, and geometry. A short device irradiation, carried out in parallel with the ToF campaign, demonstrates measurable response under CN beam conditions and confirms the practical usability of the beam for low-MeV
neutron studies. Together, these results establish the current operational performance of the CN 0{\deg} forward quasi-monoenergetic
neutron capability in the 0.7–4 MeV range and identify the steps required toward routine calibrated operation.
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All authors Jeffery Wyss, Pierfrancesco Mastinu, Elizabeth Musacchio González, Guido Martín Hernández, Luca Silvestrin, Alberto Monetti, Hans Th. J. Steiger, Manuel Böhles, B. Lalremruata, Saulo Gabriel Alberton, David Flechas
Fluid Dynamics
Intermittent two-phase flow in porous media: insights from pore-scale direct numerical simulation
Alexandra Karabasova, Sajjad Foroughi, Martin J. Blunt et al. (+1 more)
Recent X-ray imaging experiments have revealed that multiphase flow through porous media involves transient fluctuations in local occupancy, even under fixed macroscopic steady-state conditions where capillary forces dominate at the pore scale. To examine how intermittency manifests at the pore scale we perform direct numerical finite volume simulations (DNS) of immiscible two-phase flow through a micro-CT-derived Bentheimer sandstone geometry at capillary numbers in the Darcy and intermittent flow regimes. We show that intermittent disconnection and reconnection are accompanied by strongly coupled local pressure redistribution and non-wetting phase flow. This behaviour contrasts with the Darcy flow regime, in which the phases remain predominantly in fixed pathways. Macroscopically the computed pressure-gradient-capillary-number relationship ($\nabla P$-Ca) recovers both the linear Darcy and the sub-linear intermittent scaling regimes consistent with previous experimental measurements. We show how an increase in intermittency leads to the transition from the linear to the sub-linear regime. Using
topology-aware snap-off detection, we show that the spatial extent of intermittency increases with capillary number. Spectral, local-geometry, and network-connectivity analyses provide further evidence that the intermittent elements organise into connected conduits embedded within a stable backbone of fixed flow pathways: intermittency is a network-coupled rather than purely local process. This work characterises the pore-scale manifestation of intermittency as a periodic sequence of drainage and imbibition displacements triggered by local pressure fluctuations whose macroscopic consequence is to improve the overall mobility of the fluid phases.
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All authors Alexandra Karabasova, Sajjad Foroughi, Martin J. Blunt, Branko Bijeljic
Chemical Physics
Background-free measurement of exciton-exciton annihilation by two-quantum fluorescence-detected pump-probe spectroscopy
Ajay Jayachandran, Stefan Mueller, Christoph Lambert et al. (+1 more)
We introduce two-
quantum (2Q) fluorescence-detected pump-probe (F-PP) spectroscopy as a tool to probe ultrafast multiparticle interactions in many-body systems. We describe a pulse-shaper-based fully collinear setup utilizing phase cycling to capture the 2Q F-PP signal simultaneously with the one-
quantum (1Q) F-PP signal. Thus, we investigate the dynamics of energy transfer and
diffusion-limited annihilation. We apply a data post-processing strategy to isolate excited-state dynamics from spurious background. The technique is applied to a squaraine heterodimer and a squaraine copolymer to demonstrate the removal of so-called incoherent mixing that generally plagues action-detected nonlinear spectroscopy on multichromophoric systems. Specifically, we show that this approach is not only applicable to 1Q but also to 2Q F-PP signals, eliminating incoherent mixing contributions as well as other "parasitic" signals that result from pulse-overlap ambiguities. As a result, we retrieve background-free spectral and dynamical information of doubly excited electronic states.
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All authors Ajay Jayachandran, Stefan Mueller, Christoph Lambert, Tobias Brixner
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Tailoring the material properties, nanostructure and grain alignment of Alnico magnets through micromagnetic simulations
Anda Elena Stanciu, Johann Fischbacher, Markus Gusenbauer et al. (+9 more)
Alnico magnets have gained renewed interest in the search for rare-earth free permanent magnets due to their high thermal stability and magnetisation. However, the limited coercivity of these shape-anisotropy-based
alloys constrains their performance. Starting from a reference Alnico sample, we realised a finite elements micromagnetic study of exchange-decoupled rods by varying their dimensions and interrod spacing across those observed experimentally. We computed the hysteresis properties by progressing from micromagnetic simulations of a small number of rods within the magnetostatic field of their neighbours to large systems treated statistically based on the distribution of orientations of the grains. We compared the coercivity of an isolated rod with that of the exchange-decoupled system to highlight the effect of magnetostatic interactions. We computed analytically the stray field acting on a single rod as a consequence of its surrounding rods in order to confirm the scaling of the coercivity with the packing fraction p. We explored how intrinsic material properties influence
magnetic behaviour by examining materials with different magnetocrystalline anisotropy constants and saturation polarisation values. Results from several hundred simulations were used to train a multi-layer perceptron regressor and predict the
magnetic properties as function of the dimensions of the rods, interrod spacing and orientation of the grains. With this approach, we highlight the underlying trends by which nanoscale structuring, intrinsic material properties and grain
alignment can be tailored to improve the
magnetic properties of Alnico
alloys.
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All authors Anda Elena Stanciu, Johann Fischbacher, Markus Gusenbauer, Alexander Kovacs, Harald Oezelt, Joachim Seland Graff, Patricia Carvalho, Anette Eleonora Gunnæs, Matej Zaplotnik, Espen Sagvolden, Spyros Diplas, Thomas Schrefl
Fluid Dynamics
High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning
Ricard Montalà, Bernat Font, Oriol Lehmkuhl et al. (+2 more)
This study investigates active flow control (AFC) of a 30P30N high-lift wing at a Reynolds number Re$_c$ = 450,000 and angle of attack $\alpha$ = 23$^\circ$ using wallresolved large-eddy simulations (LES). Two optimization strategies are explored: open-loop
Bayesian optimization (BO) and closed-loop deep reinforcement learning (DRL), both targeting the mitigation of stall and the improvement of aerodynamic efficiency via synthetic jets on the slat, main, and flap elements. The uncontrolled configuration was validated against literature data, confirming the reliability of the LES setup. The BO framework successfully identified steady jet velocities that increased efficiency by +10.9% through a -9.7% drag reduction while maintaining lift. In contrast, the DRL
agent, despite leveraging instantaneous flow information from distributed sensors, achieved only minor improvements in lift and drag, with negligible efficiency gain. Training analysis indicated that the penalty-dominated reward constrained exploration. These results highlight the need for carefully designed rewards and computational acceleration strategies in DRL-based flow control at high Reynolds numbers.
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All authors Ricard Montalà, Bernat Font, Oriol Lehmkuhl, Ricardo Vinuesa, Ivette Rodriguez
Fluid Dynamics
Nonlinear synthetic Schlieren methods for free-surface topography measurement using telecentric imaging
Shimin Zhang, Frédéric Moisy, Wietze Herreman et al. (+1 more)
Free-surface synthetic Schlieren (FS-SS) is a high-resolution, refraction-based optical technique for measuring the instantaneous elevation of a
liquid interface. Under the assumptions of small amplitude, small slope, and small paraxial angle, the method yields a linear relationship between the gradient of the surface elevation and the apparent displacement field of a refracted pattern imaged through the surface. Here, we propose three new, nonlinear extensions of the FS-SS method that are specifically dedicated to telecentric imaging. Paraxial distortions are eliminated with a telecentric lens, thereby simplifying the optical model. This allows us to derive nonlinear surface reconstruction models that reach beyond the usual limits of small slope and small wave-magnitudes. We implement these nonlinear surface reconstruction
algorithms and compare them to the original, linear reconstruction
algorithm in three different experiments, using a solid glass lens, spreading oil drops and nonlinear Faraday waves. At the price of a few iterations, we can realise nonlinear surface reconstructions that are more precise, in particular when we reach high slopes or high amplitude regimes. We share a library that encodes these nonlinear surface reconstruction
algorithms.
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All authors Shimin Zhang, Frédéric Moisy, Wietze Herreman, Zhiliang Lin
Atmospheric and Oceanic Physics
Assessment of cloud and associated radiation fields from a GAN stochastic cloud subcolumn generator
Dongmin Lee, Lazaros Oreopoulos, Nayeong Cho et al. (+1 more)
Modern Earth System Models (ESMs) operate on horizontal scales far larger than typical cloud features, requiring
stochastic subcolumn generators to represent subgrid horizontal and vertical cloud variability. Traditional physically-based generators often rely on analytical cloud overlap paradigms, such as exponential-random decorrelation, which can struggle to capture the complex, anti-correlated behavior of non-contiguous cloud layers. In this study, we introduce a novel two-stage machine learning subcolumn generator for the GEOS atmospheric model, utilizing a Conditional Variational Autoencoder combined with a Generative
Adversarial Network (CVAE-GAN) and a U-Net architecture. Trained on a merged CloudSat-CALIPSO height-resolved cloud optical depth
dataset, the ML generator creates 56
stochastic subcolumns representing cloud occurrence and optical depth profiles. Evaluated against the established Räisänen, the ML approach accurately reproduces bimodal cloud overlap distributions, significantly reduces biases in grid-mean statistics, and halves the root-mean-square error in ISCCP-style cloud-top pressure and optical thickness joint histograms. The improvements brought by our deep generative models translate into more accurate offline radiative transfer calculations, reducing the global-mean shortwave top-of-atmosphere cloud radiative effect bias by a factor of three. Provided that the generator can be accelerated on CPUs, this offers a practical pathway to reduce structural errors at the cloud-radiation interface.
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All authors Dongmin Lee, Lazaros Oreopoulos, Nayeong Cho, Daeho Jin
Optics
Strong light enhancement by combining the photonic nanojet and plasmons from the nano-engineered microsphere
Vlatko Gašparić, Mile Ivanda, Davor Ristić et al. (+3 more)
Today's cutting-edge optical spectroscopic exploratory tools, such as Raman or infrared spectroscopy, rely on methods of signal enhancement as a route for their development. These methods are indispensable for substance identification and characterization in almost any scientific, regulatory, or industrial laboratory, therefore new and better methods of enhancement are always sought after. In this paper, the design of a new optical device for enhancement is presented, called nano-engineered microsphere (NMS). This device innovatively combines plasmons, a present flagship enhancement method, with a photonic nanojet, a new and emerging enhancement tool, to provide unprecedented properties in terms of affordability, stability, and performance. By using numerical simulations, a detailed design of the device is presented, and the optimization of device parameters for the strongest enhancement is investigated. The simulations show different influences of the parameters on the enhancement, from low to critical. The most influential parameter was found to be the radius of the nanoelement tip, which, at low values, showed a tremendous increase in the enhancement. The optimized device shows exceptionally promising abilities regarding the enhancement, while the estimated cost of production and use is low. Such properties paired with low price and ease of usage could enable the NMS to become one of the leading methods of enhancement in Raman and infrared spectroscopy with the spatial resolution towards nanometers.
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All authors Vlatko Gašparić, Mile Ivanda, Davor Ristić, Tamás Váczi, Hrvoje Gebavi, Miklós Veres
Instrumentation and Detectors
TPA-TCT analysis of the RD50-MPW4 monolithic pixel particle detector
Francisco Rogelio Palomo, Jorge Jiménez-Sánchez, Moritz Wiehe et al. (+10 more)
The RD50-MPW4, a Depleted Monolithic Active Pixel Sensor (DMAPS) was analyzed using a Two
Photon Absorption Transient Current Technique (TPA-TCT). This technique provides sensitivity maps with micrometer-scale spatial resolution, enabling the resolution of the boundaries of the detector's sensitive volume, even for small-area pixels (62 x 62 $\mu$m$^2$ in this study). With a 3D resolution, the depletion depth, the boundaries of the detector electric field, the 3D hit detection efficiency and the charge sharing between neighboring pixels were measured. The RD50-MPW4, a multi-project wafer chip developed by the HV-CMOS working group within the CERN RD50 collaboration, features a 64 x 64 DMAPS pixel
matrix. Illuminating the chip from the backside, the TPA-TCT technique can characterize any pixel element in the
matrix because silicon is transparent for near infrared
laser light (1550 nm). Electron-hole pairs are generated only around the light focal point, deep in the silicon, so that any charge collected is precisely only from the focal point. With the TPA-TCT technique, the RD50-MPW4 was found to be have a 100% hit detection efficiency under specified conditions and an effective depletion depth of 226 $\mu$m. It was also found that part of the charge in the periphery of the pixel was collected in the neighboring pixel. A 3D map of the sensor clearly shows the in-pixel electronics and the limits of the depletion region.
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All authors Francisco Rogelio Palomo, Jorge Jiménez-Sánchez, Moritz Wiehe, Jory Sonneveld, Bernhard Pilsl, Fernando Muñoz-Chavero, Raimon Casanova, Christian Irmler, Patrick Sieberer, Chenfan Zhang, Sinuo Zhang, Eva Vilella, Michael Moll
Chemical Physics
Poisoning mechanism of ammonia on proton transport and ionomer structure in cathode catalyst layer of PEM fuel cells
Yichao Huang, Zhen Zeng, Tianyou Wang et al. (+1 more)
Ammonia has strong poisoning effects on cathode catalyst layers of
proton exchange membrane (PEM) fuel
cells, but the poisoning mechanism is still unclear. In this study, all-atom molecular dynamics simulations are employed to investigate the poisoning mechanisms of ammonia. The results show that ammonium can replace the hydronium ions at the charged sites of sulfonic acid group of the ionomer side chain, and the adsorption of ammonium to sulfonic acid group can be attributed to van der Waals force and electrostatic interaction. Furthermore, other ammonia derivatives, amino and imino ions, can capture hydronium ions to form ion clusters. These ion clusters have strong capability to absorb hydronium ions, and their structures change with ammonia content and temperature. The main mechanism of formation of these clusters is due to the formation of relatively stable hydrogen bonds between ions within the clusters. These mechanisms significantly reduce the efficiency of
proton transport, thereby decreasing the catalyst layer's performance in electrochemical reactions. We also discover that the increase in temperature leads to the dissociation of large ion clusters, the blockage in the ionomer layer can be alleviated, and the
proton transport efficiency can be restored. The understanding of the poisoning mechanisms obtained in this study is helpful for subsequent research aimed at resolving ammonia poisoning and enhancing the anti-poisoning performance of catalyst layers.
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All authors Yichao Huang, Zhen Zeng, Tianyou Wang, Zhizhao Che